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Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
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Thinet: A filter level pruning method for deep neural network compression
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Exploring sparsity in recurrent neural networks
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Structured bayesian pruning via log-normal multiplicative noise
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Designing energy-efficient convolutional neural networks using energy-aware pruning
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